





'''simple docstring'''






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=99		,   a_=13		,   a_=7		,   a_=9		,   a_=True		,   a_=True		,   a_=False		,   a_=32		,   a_=5		,   a_=4		,   a_=37		,   a_=8		,   a_=0.1		,   a_=0.002		,   a_=1		,   a_=0		,   a_=0		,   a_=None		,   a_=None		,   ):
			lowerCAmelCase  :     Optional[Any]       						= parent
			lowerCAmelCase  :     Tuple       						= batch_size
			lowerCAmelCase  :     Optional[Any]       						= encoder_seq_length
			lowerCAmelCase  :     List[Any]       						= decoder_seq_length
			# For common tests
			lowerCAmelCase  :     Union[str, Any]       						= self.decoder_seq_length
			lowerCAmelCase  :     int       						= is_training
			lowerCAmelCase  :     Dict       						= use_attention_mask
			lowerCAmelCase  :     Optional[Any]       						= use_labels
			lowerCAmelCase  :     List[Any]       						= vocab_size
			lowerCAmelCase  :     Optional[Any]       						= hidden_size
			lowerCAmelCase  :     Union[str, Any]       						= num_hidden_layers
			lowerCAmelCase  :     Union[str, Any]       						= num_attention_heads
			lowerCAmelCase  :     List[str]       						= d_ff
			lowerCAmelCase  :     Union[str, Any]       						= relative_attention_num_buckets
			lowerCAmelCase  :     List[str]       						= dropout_rate
			lowerCAmelCase  :     str       						= initializer_factor
			lowerCAmelCase  :     Optional[int]       						= eos_token_id
			lowerCAmelCase  :     Optional[int]       						= pad_token_id
			lowerCAmelCase  :     int       						= decoder_start_token_id
			lowerCAmelCase  :     Optional[Any]       						= None
			lowerCAmelCase  :     List[Any]       						= decoder_layers



	def 	_lowerCamelCase     ( self						):
			return TaConfig.from_pretrained("google/umt5-base"						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   ):
			if attention_mask is None:
					lowerCAmelCase  :     Optional[Any]       						= input_ids.ne(config.pad_token_id						)
			if decoder_attention_mask is None:
					lowerCAmelCase  :     Dict       						= decoder_input_ids.ne(config.pad_token_id						)
			if head_mask is None:
					lowerCAmelCase  :     Union[str, Any]       						= torch.ones(config.num_hidden_layers		,   config.num_attention_heads		,   device=a_						)
			if decoder_head_mask is None:
					lowerCAmelCase  :     int       						= torch.ones(config.num_decoder_layers		,   config.num_attention_heads		,   device=a_						)
			if cross_attn_head_mask is None:
					lowerCAmelCase  :     Optional[int]       						= torch.ones(
					    config.num_decoder_layers		,   config.num_attention_heads		,   device=a_						)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= ids_tensor([self.batch_size, self.encoder_seq_length]		,   self.vocab_size						)
			lowerCAmelCase  :     List[str]       						= ids_tensor([self.batch_size, self.decoder_seq_length]		,   self.vocab_size						)

			# we need to clamp the input ids here to avoid having pad token in between
			# this is because for NllbMoe the position_ids are prepared such that
			# all pad tokens have pos id = 2 and rest are between 2..seq_length
			# and the seq_length here is seq_length - num_pad_tokens
			# but when using past, there is no way of knowing if the past input ids had
			# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
			# position_ids being off by num_pad_tokens in past input
			lowerCAmelCase  :     List[str]       						= input_ids.clamp(self.pad_token_id + 1						)
			lowerCAmelCase  :     Any       						= decoder_input_ids.clamp(self.pad_token_id + 1						)

			lowerCAmelCase  :     Union[str, Any]       						= self.get_config()
			lowerCAmelCase  :     Optional[Any]       						= config.num_attention_heads
			lowerCAmelCase  :     List[str]       						= self.prepare_inputs_dict(a_		,   a_		,   a_						)
			return config, input_dict



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     int       						= self.prepare_config_and_inputs()
			return config, inputs_dict



	def 	_lowerCamelCase     ( self						):
			return TaConfig(
			    vocab_size=166		,   d_model=self.hidden_size		,   d_ff=self.d_ff		,   d_kv=self.hidden_size // self.num_attention_heads		,   num_layers=self.num_hidden_layers		,   num_decoder_layers=self.decoder_layers		,   num_heads=self.num_attention_heads		,   relative_attention_num_buckets=self.relative_attention_num_buckets		,   dropout_rate=self.dropout_rate		,   initializer_factor=self.initializer_factor		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.pad_token_id		,   pad_token_id=self.pad_token_id		,   decoder_start_token_id=self.decoder_start_token_id		,   )



	def 	_lowerCamelCase     ( self						):
			return TaConfig(
			    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   d_ff=self.d_ff		,   d_kv=self.hidden_size // self.num_attention_heads		,   num_layers=self.num_hidden_layers		,   num_decoder_layers=self.decoder_layers		,   num_heads=self.num_attention_heads		,   relative_attention_num_buckets=self.relative_attention_num_buckets		,   dropout_rate=self.dropout_rate		,   initializer_factor=self.initializer_factor		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.pad_token_id		,   pad_token_id=self.pad_token_id		,   decoder_start_token_id=self.decoder_start_token_id		,   )



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     List[str]       						= UMTaModel(config=a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     List[Any]       						= model(
			    input_ids=a_		,   decoder_input_ids=a_		,   attention_mask=a_		,   decoder_attention_mask=a_		,   )
			lowerCAmelCase  :     Optional[int]       						= model(input_ids=a_		,   decoder_input_ids=a_						)
			lowerCAmelCase  :     str       						= result.last_hidden_state
			lowerCAmelCase  :     str       						= result.past_key_values
			lowerCAmelCase  :     Optional[int]       						= result.encoder_last_hidden_state

			self.parent.assertEqual(encoder_output.size()		,   (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
			self.parent.assertEqual(decoder_output.size()		,   (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
			# There should be `num_layers` key value embeddings stored in decoder_past
			self.parent.assertEqual(len(a_						)		,   config.num_layers						)
			# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
			self.parent.assertEqual(len(decoder_past[0]						)		,   4						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     str       						= UMTaModel(config=a_						).get_decoder().to(a_						).eval()
			# first forward pass
			lowerCAmelCase  :     Union[str, Any]       						= model(a_		,   use_cache=a_						)
			lowerCAmelCase  :     Any       						= model(a_						)
			lowerCAmelCase  :     int       						= model(a_		,   use_cache=a_						)

			self.parent.assertTrue(len(a_						) == len(a_						)						)
			self.parent.assertTrue(len(a_						) == len(a_						) + 1						)

			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			lowerCAmelCase  :     Optional[int]       						= ids_tensor((self.batch_size, 1)		,   config.vocab_size						)

			# append to next input_ids and
			lowerCAmelCase  :     Optional[int]       						= torch.cat([input_ids, next_tokens]		,   dim=-1						)

			lowerCAmelCase  :     Union[str, Any]       						= model(a_						)["last_hidden_state"]
			lowerCAmelCase  :     str       						= model(a_		,   past_key_values=a_						)["last_hidden_state"]

			# select random slice
			lowerCAmelCase  :     List[str]       						= ids_tensor((1,)		,   output_from_past.shape[-1]						).item()
			lowerCAmelCase  :     List[str]       						= output_from_no_past[:, -1, random_slice_idx].detach()
			lowerCAmelCase  :     Dict       						= output_from_past[:, 0, random_slice_idx].detach()

			# test that outputs are equal for slice
			self.parent.assertTrue(torch.allclose(a_		,   a_		,   atol=1e-3						)						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   ):
			lowerCAmelCase  :     Any       						= UMTaModel(config=a_						).to(a_						).half().eval()
			lowerCAmelCase  :     Union[str, Any]       						= model(**a_						)["last_hidden_state"]
			self.parent.assertFalse(torch.isnan(a_						).any().item()						)






@require_torch
class 		lowerCamelCase	(   _A   , _A   , _A   , unittest.TestCase							):
	snake_case_   = (
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	snake_case_   = (UMTaForConditionalGeneration,) if is_torch_available() else ()
	snake_case_   = (
	    {
	        "conversational": UMTaForConditionalGeneration,
	        "feature-extraction": UMTaModel,
	        "summarization": UMTaForConditionalGeneration,
	        "text2text-generation": UMTaForConditionalGeneration,
	        "translation": UMTaForConditionalGeneration,
	        "question-answering": UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_   = True
	snake_case_   = False
	snake_case_   = False
	snake_case_   = True
	snake_case_   = True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	snake_case_   = [0.8, 0.9]



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= UMTaModelTester(self						)



	@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= self.model_tester.prepare_config_and_inputs()
			lowerCAmelCase  :     List[str]       						= UMTaModel(config_and_inputs[0]						).to(a_						)
			with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    a_		,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,   F'''{tmpdirname}/t5_test.onnx'''		,   export_params=a_		,   opset_version=9		,   input_names=["input_ids", "decoder_input_ids"]		,   )



	@unittest.skipIf(torch_device == "cpu"		,   "Cant do half precision"						)
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= ["encoder_attentions", "decoder_attentions", "cross_attentions"]
			lowerCAmelCase  :     List[Any]       						= self.model_tester.prepare_config_and_inputs()
			lowerCAmelCase  :     Dict       						= config_and_inputs[0]
			lowerCAmelCase  :     str       						= UMTaForConditionalGeneration(a_						).eval()
			model.to(a_						)

			lowerCAmelCase  :     Any       						= {
			    "head_mask": torch.zeros(config.num_layers		,   config.num_heads		,   device=a_						),
			    "decoder_head_mask": torch.zeros(config.num_decoder_layers		,   config.num_heads		,   device=a_						),
			    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers		,   config.num_heads		,   device=a_						),
			}

			for attn_name, (name, mask) in zip(a_		,   head_masking.items()						):
					lowerCAmelCase  :     Optional[Any]       						= {name: mask}
					# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
					if name == "head_mask":
							lowerCAmelCase  :     List[Any]       						= torch.ones(
							    config.num_decoder_layers		,   config.num_heads		,   device=a_						)

					lowerCAmelCase  :     List[str]       						= model.generate(
					    config_and_inputs[1]["input_ids"]		,   num_beams=1		,   max_length=3		,   output_attentions=a_		,   return_dict_in_generate=a_		,   **a_		,   )
					# We check the state of decoder_attentions and cross_attentions just from the last step
					lowerCAmelCase  :     Any       						= out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
					self.assertEqual(sum([w.sum().item() for w in attn_weights]						)		,   0.0						)





	@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
	def 	_lowerCamelCase     ( self						):
			pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 		lowerCamelCase	(   unittest.TestCase							):



	@slow
	@unittest.skip(
	    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,   return_dict=a_						).to(a_						)
			lowerCAmelCase  :     Any       						= AutoTokenizer.from_pretrained("google/umt5-small"		,   use_fast=a_		,   legacy=a_						)
			lowerCAmelCase  :     Dict       						= [
			    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
			    "No se como puedo <extra_id_0>.",
			    "This is the reason why we <extra_id_0> them.",
			    "The <extra_id_0> walks in <extra_id_1>, seats",
			    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
			]
			lowerCAmelCase  :     Dict       						= tokenizer(a_		,   return_tensors="pt"		,   padding=a_						).input_ids
			# fmt: off
			lowerCAmelCase  :     Optional[int]       						= torch.tensor(
			    [
			        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
			        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
			    ]						)
			# fmt: on
			torch.testing.assert_allclose(a_		,   a_						)

			lowerCAmelCase  :     Optional[Any]       						= model.generate(input_ids.to(a_						)						)
			lowerCAmelCase  :     List[str]       						= [
			    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
			    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
			    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
			    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
			    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
			]
			lowerCAmelCase  :     Tuple       						= tokenizer.batch_decode(a_						)
			self.assertEqual(a_		,   a_						)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



